Industry increasingly depends upon highly automated data acquisition and control systems to ensure that industrial processes are run efficiently, safely and reliably while lowering their overall production costs. Data acquisition takes a variety of forms, including trending and non-trending. Trending data generally comprises the type acquired when a number of sensors measure aspects of an industrial process and periodically report their measurements back to a data collection and control system. By way of example the trending data produced by a sensor/recorder include: a temperature, a pressure, a pH, a mass/volume flow of material, a tallied inventory of packages waiting in a shipping line. If one or more pieces of trending data for a process variable are not stored, they can generally be estimated by observing the values assigned to the variable before and after the lost values, and then interpolating between the points.
Non-trending data, on the other hand, does not follow a pattern from point to point, and therefore cannot be estimated from nearest neighbor data points. Production data identifying general production requests (e.g., create a batch of chocolate milk) and tasks performed within the scope of each general production request, is an example of non-trending data. In view of the inability to estimate the values attributed to lost non-trending data, specialized databases, referred to as production event servers, have been developed to receive and maintain detailed production event histories.